Overview

Dataset statistics

Number of variables15
Number of observations1194
Missing cells542
Missing cells (%)3.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory140.0 KiB
Average record size in memory120.1 B

Variable types

Text3
Categorical2
Numeric10

Alerts

Total is highly overall correlated with HP and 7 other fieldsHigh correlation
HP is highly overall correlated with Total and 3 other fieldsHigh correlation
Attack is highly overall correlated with Total and 5 other fieldsHigh correlation
Defense is highly overall correlated with Total and 4 other fieldsHigh correlation
Sp. Atk is highly overall correlated with Total and 3 other fieldsHigh correlation
Sp. Def is highly overall correlated with Total and 4 other fieldsHigh correlation
Speed is highly overall correlated with Total and 2 other fieldsHigh correlation
Average Stats is highly overall correlated with Total and 7 other fieldsHigh correlation
Atk-Def Ratio is highly overall correlated with AttackHigh correlation
Rank is highly overall correlated with Total and 7 other fieldsHigh correlation
Type2 has 542 (45.4%) missing valuesMissing
Names has unique valuesUnique
Image_URL has unique valuesUnique

Reproduction

Analysis started2023-07-16 10:29:56.084228
Analysis finished2023-07-16 10:30:03.350829
Duration7.27 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Names
Text

UNIQUE 

Distinct1194
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2023-07-16T16:00:03.644407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length36
Median length31
Mean length9.741206
Min length3

Characters and Unicode

Total characters11631
Distinct characters61
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1194 ?
Unique (%)100.0%

Sample

1st rowBulbasaur
2nd rowIvysaur
3rd rowVenusaur
4th rowMega Venusaur
5th rowCharmander
ValueCountFrequency (%)
mega 48
 
3.1%
form 26
 
1.7%
forme 25
 
1.6%
galarian 20
 
1.3%
alolan 18
 
1.1%
hisuian 16
 
1.0%
size 8
 
0.5%
iron 8
 
0.5%
style 6
 
0.4%
mode 6
 
0.4%
Other values (1129) 1389
88.5%
2023-07-16T16:00:04.063789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1095
 
9.4%
e 953
 
8.2%
o 882
 
7.6%
r 810
 
7.0%
i 751
 
6.5%
734
 
6.3%
l 638
 
5.5%
n 612
 
5.3%
t 469
 
4.0%
u 404
 
3.5%
Other values (51) 4283
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9302
80.0%
Uppercase Letter 1566
 
13.5%
Space Separator 734
 
6.3%
Dash Punctuation 16
 
0.1%
Other Punctuation 10
 
0.1%
Other Symbol 2
 
< 0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1095
11.8%
e 953
10.2%
o 882
 
9.5%
r 810
 
8.7%
i 751
 
8.1%
l 638
 
6.9%
n 612
 
6.6%
t 469
 
5.0%
u 404
 
4.3%
s 341
 
3.7%
Other values (17) 2347
25.2%
Uppercase Letter
ValueCountFrequency (%)
S 198
12.6%
M 167
 
10.7%
C 110
 
7.0%
G 106
 
6.8%
F 104
 
6.6%
P 89
 
5.7%
T 87
 
5.6%
A 82
 
5.2%
B 80
 
5.1%
D 78
 
5.0%
Other values (16) 465
29.7%
Other Punctuation
ValueCountFrequency (%)
. 5
50.0%
' 4
40.0%
: 1
 
10.0%
Other Symbol
ValueCountFrequency (%)
♂ 1
50.0%
♀ 1
50.0%
Space Separator
ValueCountFrequency (%)
734
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10868
93.4%
Common 763
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1095
 
10.1%
e 953
 
8.8%
o 882
 
8.1%
r 810
 
7.5%
i 751
 
6.9%
l 638
 
5.9%
n 612
 
5.6%
t 469
 
4.3%
u 404
 
3.7%
s 341
 
3.1%
Other values (43) 3913
36.0%
Common
ValueCountFrequency (%)
734
96.2%
- 16
 
2.1%
. 5
 
0.7%
' 4
 
0.5%
: 1
 
0.1%
2 1
 
0.1%
♂ 1
 
0.1%
♀ 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11627
> 99.9%
None 2
 
< 0.1%
Misc Symbols 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1095
 
9.4%
e 953
 
8.2%
o 882
 
7.6%
r 810
 
7.0%
i 751
 
6.5%
734
 
6.3%
l 638
 
5.5%
n 612
 
5.3%
t 469
 
4.0%
u 404
 
3.5%
Other values (48) 4279
36.8%
None
ValueCountFrequency (%)
é 2
100.0%
Misc Symbols
ValueCountFrequency (%)
♂ 1
50.0%
♀ 1
50.0%

Type1
Categorical

Distinct18
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Water
150 
Normal
131 
Grass
105 
Bug
91 
Psychic
82 
Other values (13)
635 

Length

Max length8
Median length7
Mean length5.2914573
Min length3

Characters and Unicode

Total characters6318
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrass
2nd rowGrass
3rd rowGrass
4th rowGrass
5th rowFire

Common Values

ValueCountFrequency (%)
Water 150
12.6%
Normal 131
11.0%
Grass 105
 
8.8%
Bug 91
 
7.6%
Psychic 82
 
6.9%
Fire 75
 
6.3%
Electric 73
 
6.1%
Rock 67
 
5.6%
Dark 56
 
4.7%
Fighting 50
 
4.2%
Other values (8) 314
26.3%

Length

2023-07-16T16:00:04.161523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
water 150
12.6%
normal 131
11.0%
grass 105
 
8.8%
bug 91
 
7.6%
psychic 82
 
6.9%
fire 75
 
6.3%
electric 73
 
6.1%
rock 67
 
5.6%
dark 56
 
4.7%
fighting 50
 
4.2%
Other values (8) 314
26.3%

Most occurring characters

ValueCountFrequency (%)
r 716
 
11.3%
a 522
 
8.3%
o 430
 
6.8%
e 427
 
6.8%
c 420
 
6.6%
i 416
 
6.6%
s 384
 
6.1%
t 363
 
5.7%
l 257
 
4.1%
g 250
 
4.0%
Other values (18) 2133
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5124
81.1%
Uppercase Letter 1194
 
18.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 716
14.0%
a 522
10.2%
o 430
8.4%
e 427
8.3%
c 420
8.2%
i 416
8.1%
s 384
7.5%
t 363
 
7.1%
l 257
 
5.0%
g 250
 
4.9%
Other values (7) 939
18.3%
Uppercase Letter
ValueCountFrequency (%)
G 198
16.6%
F 166
13.9%
W 150
12.6%
N 131
11.0%
P 127
10.6%
D 105
8.8%
B 91
7.6%
E 73
 
6.1%
R 67
 
5.6%
I 43
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 6318
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 716
 
11.3%
a 522
 
8.3%
o 430
 
6.8%
e 427
 
6.8%
c 420
 
6.6%
i 416
 
6.6%
s 384
 
6.1%
t 363
 
5.7%
l 257
 
4.1%
g 250
 
4.0%
Other values (18) 2133
33.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6318
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 716
 
11.3%
a 522
 
8.3%
o 430
 
6.8%
e 427
 
6.8%
c 420
 
6.6%
i 416
 
6.6%
s 384
 
6.1%
t 363
 
5.7%
l 257
 
4.1%
g 250
 
4.0%
Other values (18) 2133
33.8%

Type2
Categorical

MISSING 

Distinct18
Distinct (%)2.8%
Missing542
Missing (%)45.4%
Memory size9.5 KiB
Flying
122 
Psychic
49 
Poison
47 
Ground
43 
Fairy
42 
Other values (13)
349 

Length

Max length8
Median length7
Mean length5.6058282
Min length3

Characters and Unicode

Total characters3655
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoison
2nd rowPoison
3rd rowPoison
4th rowPoison
5th rowFlying

Common Values

ValueCountFrequency (%)
Flying 122
 
10.2%
Psychic 49
 
4.1%
Poison 47
 
3.9%
Ground 43
 
3.6%
Fairy 42
 
3.5%
Fighting 41
 
3.4%
Steel 40
 
3.4%
Dragon 39
 
3.3%
Ghost 37
 
3.1%
Dark 33
 
2.8%
Other values (8) 159
 
13.3%
(Missing) 542
45.4%

Length

2023-07-16T16:00:04.223784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
flying 122
18.7%
psychic 49
 
7.5%
poison 47
 
7.2%
ground 43
 
6.6%
fairy 42
 
6.4%
fighting 41
 
6.3%
steel 40
 
6.1%
dragon 39
 
6.0%
ghost 37
 
5.7%
grass 33
 
5.1%
Other values (8) 159
24.4%

Most occurring characters

ValueCountFrequency (%)
i 375
 
10.3%
n 292
 
8.0%
r 266
 
7.3%
g 252
 
6.9%
o 250
 
6.8%
F 225
 
6.2%
y 213
 
5.8%
s 199
 
5.4%
l 193
 
5.3%
a 190
 
5.2%
Other values (18) 1200
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3003
82.2%
Uppercase Letter 652
 
17.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 375
12.5%
n 292
9.7%
r 266
8.9%
g 252
8.4%
o 250
8.3%
y 213
 
7.1%
s 199
 
6.6%
l 193
 
6.4%
a 190
 
6.3%
c 165
 
5.5%
Other values (7) 608
20.2%
Uppercase Letter
ValueCountFrequency (%)
F 225
34.5%
G 113
17.3%
P 96
14.7%
D 72
 
11.0%
S 40
 
6.1%
W 25
 
3.8%
I 22
 
3.4%
R 19
 
2.9%
N 18
 
2.8%
E 13
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3655
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 375
 
10.3%
n 292
 
8.0%
r 266
 
7.3%
g 252
 
6.9%
o 250
 
6.8%
F 225
 
6.2%
y 213
 
5.8%
s 199
 
5.4%
l 193
 
5.3%
a 190
 
5.2%
Other values (18) 1200
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3655
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 375
 
10.3%
n 292
 
8.0%
r 266
 
7.3%
g 252
 
6.9%
o 250
 
6.8%
F 225
 
6.2%
y 213
 
5.8%
s 199
 
5.4%
l 193
 
5.3%
a 190
 
5.2%
Other values (18) 1200
32.8%

Total
Real number (ℝ)

HIGH CORRELATION 

Distinct228
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean441.20687
Minimum175
Maximum1125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-16T16:00:04.289321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum175
5-th percentile250
Q1330
median460.5
Q3520
95-th percentile630
Maximum1125
Range950
Interquartile range (IQR)190

Descriptive statistics

Standard deviation121.01533
Coefficient of variation (CV)0.27428251
Kurtosis0.16419476
Mean441.20687
Median Absolute Deviation (MAD)79.5
Skewness0.1463704
Sum526801
Variance14644.709
MonotonicityNot monotonic
2023-07-16T16:00:04.365067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 47
 
3.9%
500 37
 
3.1%
490 33
 
2.8%
580 33
 
2.8%
570 30
 
2.5%
300 27
 
2.3%
530 26
 
2.2%
405 26
 
2.2%
480 23
 
1.9%
485 22
 
1.8%
Other values (218) 890
74.5%
ValueCountFrequency (%)
175 1
 
0.1%
180 2
 
0.2%
185 1
 
0.1%
190 1
 
0.1%
194 1
 
0.1%
195 3
0.3%
198 1
 
0.1%
200 4
0.3%
205 5
0.4%
210 7
0.6%
ValueCountFrequency (%)
1125 1
 
0.1%
780 3
 
0.3%
770 2
 
0.2%
754 1
 
0.1%
720 1
 
0.1%
708 1
 
0.1%
700 11
0.9%
690 1
 
0.1%
680 21
1.8%
670 6
 
0.5%

HP
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.883585
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-16T16:00:04.438366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38
Q152
median70
Q385
95-th percentile110.35
Maximum255
Range254
Interquartile range (IQR)33

Descriptive statistics

Standard deviation26.86174
Coefficient of variation (CV)0.37895572
Kurtosis7.1887805
Mean70.883585
Median Absolute Deviation (MAD)15
Skewness1.6424048
Sum84635
Variance721.55307
MonotonicityNot monotonic
2023-07-16T16:00:04.617791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 89
 
7.5%
70 88
 
7.4%
50 80
 
6.7%
75 63
 
5.3%
65 62
 
5.2%
80 61
 
5.1%
40 57
 
4.8%
100 52
 
4.4%
45 51
 
4.3%
55 49
 
4.1%
Other values (98) 542
45.4%
ValueCountFrequency (%)
1 1
 
0.1%
10 3
 
0.3%
20 6
 
0.5%
25 4
 
0.3%
28 2
 
0.2%
30 17
1.4%
31 1
 
0.1%
33 1
 
0.1%
35 20
1.7%
36 1
 
0.1%
ValueCountFrequency (%)
255 2
0.2%
250 1
0.1%
223 1
0.1%
216 1
0.1%
200 1
0.1%
190 1
0.1%
170 2
0.2%
165 1
0.1%
160 1
0.1%
155 1
0.1%

Attack
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.948911
Minimum5
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-16T16:00:04.695659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile30.65
Q156
median80
Q3100
95-th percentile135.35
Maximum190
Range185
Interquartile range (IQR)44

Descriptive statistics

Standard deviation32.126164
Coefficient of variation (CV)0.39686963
Kurtosis-0.097714708
Mean80.948911
Median Absolute Deviation (MAD)21
Skewness0.42931635
Sum96653
Variance1032.0904
MonotonicityNot monotonic
2023-07-16T16:00:04.771429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 65
 
5.4%
65 56
 
4.7%
80 52
 
4.4%
85 50
 
4.2%
75 49
 
4.1%
60 48
 
4.0%
50 47
 
3.9%
70 47
 
3.9%
55 47
 
3.9%
90 42
 
3.5%
Other values (116) 691
57.9%
ValueCountFrequency (%)
5 2
 
0.2%
10 3
 
0.3%
15 1
 
0.1%
20 10
0.8%
22 1
 
0.1%
23 1
 
0.1%
24 1
 
0.1%
25 8
0.7%
27 1
 
0.1%
28 1
 
0.1%
ValueCountFrequency (%)
190 1
 
0.1%
185 1
 
0.1%
181 1
 
0.1%
180 3
0.3%
170 2
 
0.2%
167 1
 
0.1%
165 4
0.3%
164 1
 
0.1%
160 7
0.6%
157 1
 
0.1%

Defense
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.587102
Minimum5
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-16T16:00:04.851805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile35
Q151.25
median70
Q390
95-th percentile130
Maximum250
Range245
Interquartile range (IQR)38.75

Descriptive statistics

Standard deviation30.678626
Coefficient of variation (CV)0.41131274
Kurtosis2.905908
Mean74.587102
Median Absolute Deviation (MAD)20
Skewness1.1357496
Sum89057
Variance941.17807
MonotonicityNot monotonic
2023-07-16T16:00:04.931848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 81
 
6.8%
60 73
 
6.1%
50 66
 
5.5%
80 63
 
5.3%
65 59
 
4.9%
90 56
 
4.7%
40 54
 
4.5%
100 53
 
4.4%
45 49
 
4.1%
55 46
 
3.9%
Other values (105) 594
49.7%
ValueCountFrequency (%)
5 2
 
0.2%
10 1
 
0.1%
15 4
 
0.3%
20 7
 
0.6%
23 1
 
0.1%
25 4
 
0.3%
28 2
 
0.2%
30 20
1.7%
31 1
 
0.1%
32 2
 
0.2%
ValueCountFrequency (%)
250 1
 
0.1%
230 3
0.3%
211 1
 
0.1%
200 2
 
0.2%
184 2
 
0.2%
180 3
0.3%
168 1
 
0.1%
160 3
0.3%
152 1
 
0.1%
150 7
0.6%

Sp. Atk
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.881072
Minimum10
Maximum194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-16T16:00:05.007410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q150
median65
Q395
95-th percentile135
Maximum194
Range184
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.702411
Coefficient of variation (CV)0.44870925
Kurtosis0.1076423
Mean72.881072
Median Absolute Deviation (MAD)20.5
Skewness0.70692421
Sum87020
Variance1069.4477
MonotonicityNot monotonic
2023-07-16T16:00:05.082199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 73
 
6.1%
60 66
 
5.5%
50 64
 
5.4%
65 62
 
5.2%
55 56
 
4.7%
45 52
 
4.4%
80 48
 
4.0%
70 47
 
3.9%
30 42
 
3.5%
95 41
 
3.4%
Other values (117) 643
53.9%
ValueCountFrequency (%)
10 4
 
0.3%
15 5
 
0.4%
20 10
 
0.8%
21 1
 
0.1%
23 1
 
0.1%
24 2
 
0.2%
25 16
 
1.3%
27 2
 
0.2%
29 6
 
0.5%
30 42
3.5%
ValueCountFrequency (%)
194 1
 
0.1%
180 3
0.3%
175 1
 
0.1%
173 1
 
0.1%
170 3
0.3%
167 1
 
0.1%
165 3
0.3%
160 2
0.2%
159 1
 
0.1%
157 1
 
0.1%

Sp. Def
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.123953
Minimum20
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-16T16:00:05.157203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile35
Q150
median70
Q390
95-th percentile120
Maximum250
Range230
Interquartile range (IQR)40

Descriptive statistics

Standard deviation27.628412
Coefficient of variation (CV)0.38306846
Kurtosis2.2699233
Mean72.123953
Median Absolute Deviation (MAD)20
Skewness0.89976542
Sum86116
Variance763.32913
MonotonicityNot monotonic
2023-07-16T16:00:05.233480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 73
 
6.1%
80 71
 
5.9%
50 68
 
5.7%
60 65
 
5.4%
55 63
 
5.3%
75 62
 
5.2%
65 62
 
5.2%
90 54
 
4.5%
40 52
 
4.4%
45 52
 
4.4%
Other values (96) 572
47.9%
ValueCountFrequency (%)
20 7
 
0.6%
23 1
 
0.1%
25 16
1.3%
30 28
2.3%
31 3
 
0.3%
32 1
 
0.1%
33 1
 
0.1%
34 1
 
0.1%
35 29
2.4%
36 2
 
0.2%
ValueCountFrequency (%)
250 1
 
0.1%
230 1
 
0.1%
200 1
 
0.1%
160 2
 
0.2%
154 3
0.3%
150 7
0.6%
142 1
 
0.1%
140 4
0.3%
138 1
 
0.1%
135 7
0.6%

Speed
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.782245
Minimum5
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-16T16:00:05.306827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile25
Q145
median67.5
Q390.75
95-th percentile120.35
Maximum200
Range195
Interquartile range (IQR)45.75

Descriptive statistics

Standard deviation30.200828
Coefficient of variation (CV)0.43278671
Kurtosis-0.16035918
Mean69.782245
Median Absolute Deviation (MAD)22.5
Skewness0.37856378
Sum83320
Variance912.09001
MonotonicityNot monotonic
2023-07-16T16:00:05.389574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 62
 
5.2%
60 61
 
5.1%
65 54
 
4.5%
70 52
 
4.4%
30 51
 
4.3%
90 46
 
3.9%
45 45
 
3.8%
100 42
 
3.5%
85 42
 
3.5%
40 41
 
3.4%
Other values (117) 698
58.5%
ValueCountFrequency (%)
5 3
 
0.3%
10 5
 
0.4%
13 1
 
0.1%
15 14
1.2%
20 21
1.8%
22 1
 
0.1%
23 4
 
0.3%
24 1
 
0.1%
25 13
1.1%
26 1
 
0.1%
ValueCountFrequency (%)
200 1
 
0.1%
180 1
 
0.1%
160 1
 
0.1%
151 1
 
0.1%
150 7
0.6%
148 1
 
0.1%
145 3
0.3%
143 1
 
0.1%
142 1
 
0.1%
140 1
 
0.1%

Average Stats
Real number (ℝ)

HIGH CORRELATION 

Distinct228
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.534478
Minimum29.166667
Maximum187.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-16T16:00:05.471113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum29.166667
5-th percentile41.666667
Q155
median76.75
Q386.666667
95-th percentile105
Maximum187.5
Range158.33333
Interquartile range (IQR)31.666667

Descriptive statistics

Standard deviation20.169221
Coefficient of variation (CV)0.27428251
Kurtosis0.16419476
Mean73.534478
Median Absolute Deviation (MAD)13.25
Skewness0.1463704
Sum87800.167
Variance406.79747
MonotonicityNot monotonic
2023-07-16T16:00:05.543756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 47
 
3.9%
83.33333333 37
 
3.1%
81.66666667 33
 
2.8%
96.66666667 33
 
2.8%
95 30
 
2.5%
50 27
 
2.3%
88.33333333 26
 
2.2%
67.5 26
 
2.2%
80 23
 
1.9%
80.83333333 22
 
1.8%
Other values (218) 890
74.5%
ValueCountFrequency (%)
29.16666667 1
 
0.1%
30 2
 
0.2%
30.83333333 1
 
0.1%
31.66666667 1
 
0.1%
32.33333333 1
 
0.1%
32.5 3
0.3%
33 1
 
0.1%
33.33333333 4
0.3%
34.16666667 5
0.4%
35 7
0.6%
ValueCountFrequency (%)
187.5 1
 
0.1%
130 3
 
0.3%
128.3333333 2
 
0.2%
125.6666667 1
 
0.1%
120 1
 
0.1%
118 1
 
0.1%
116.6666667 11
0.9%
115 1
 
0.1%
113.3333333 21
1.8%
111.6666667 6
 
0.5%
Distinct218
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2023-07-16T16:00:05.779959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length8.89866
Min length3

Characters and Unicode

Total characters10625
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)4.2%

Sample

1st rowGrass-Poison
2nd rowGrass-Poison
3rd rowGrass-Poison
4th rowGrass-Poison
5th rowFire
ValueCountFrequency (%)
water 81
 
6.8%
normal 79
 
6.6%
psychic 47
 
3.9%
grass 46
 
3.9%
fire 37
 
3.1%
electric 37
 
3.1%
normal-flying 31
 
2.6%
fighting 30
 
2.5%
bug 25
 
2.1%
ice 22
 
1.8%
Other values (208) 759
63.6%
2023-07-16T16:00:06.106915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 982
 
9.2%
i 791
 
7.4%
a 712
 
6.7%
o 680
 
6.4%
- 652
 
6.1%
e 587
 
5.5%
c 585
 
5.5%
s 583
 
5.5%
t 519
 
4.9%
g 502
 
4.7%
Other values (19) 4032
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8127
76.5%
Uppercase Letter 1846
 
17.4%
Dash Punctuation 652
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 982
12.1%
i 791
9.7%
a 712
8.8%
o 680
8.4%
e 587
 
7.2%
c 585
 
7.2%
s 583
 
7.2%
t 519
 
6.4%
g 502
 
6.2%
n 492
 
6.1%
Other values (7) 1694
20.8%
Uppercase Letter
ValueCountFrequency (%)
F 391
21.2%
G 311
16.8%
P 223
12.1%
D 177
9.6%
W 175
9.5%
N 149
 
8.1%
B 100
 
5.4%
E 86
 
4.7%
R 86
 
4.7%
S 83
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 652
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9973
93.9%
Common 652
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 982
 
9.8%
i 791
 
7.9%
a 712
 
7.1%
o 680
 
6.8%
e 587
 
5.9%
c 585
 
5.9%
s 583
 
5.8%
t 519
 
5.2%
g 502
 
5.0%
n 492
 
4.9%
Other values (18) 3540
35.5%
Common
ValueCountFrequency (%)
- 652
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 982
 
9.2%
i 791
 
7.4%
a 712
 
6.7%
o 680
 
6.4%
- 652
 
6.1%
e 587
 
5.5%
c 585
 
5.5%
s 583
 
5.5%
t 519
 
4.9%
g 502
 
4.7%
Other values (19) 4032
37.9%

Atk-Def Ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct510
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1721121
Minimum0.043478261
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-16T16:00:06.205738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.043478261
5-th percentile0.55555556
Q10.84615385
median1.0833333
Q31.42625
95-th percentile2
Maximum9
Range8.9565217
Interquartile range (IQR)0.58009615

Descriptive statistics

Standard deviation0.51567515
Coefficient of variation (CV)0.43995377
Kurtosis46.348022
Mean1.1721121
Median Absolute Deviation (MAD)0.28333333
Skewness3.812777
Sum1399.5019
Variance0.26592086
MonotonicityNot monotonic
2023-07-16T16:00:06.277860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 114
 
9.5%
1.25 23
 
1.9%
2 19
 
1.6%
0.8 18
 
1.5%
1.5 17
 
1.4%
1.666666667 14
 
1.2%
0.833333333 13
 
1.1%
1.6 13
 
1.1%
1.4 12
 
1.0%
1.333333333 12
 
1.0%
Other values (500) 939
78.6%
ValueCountFrequency (%)
0.043478261 1
0.1%
0.181818182 1
0.1%
0.221374046 1
0.1%
0.279069767 1
0.1%
0.28125 1
0.1%
0.285714286 1
0.1%
0.307692308 1
0.1%
0.30952381 1
0.1%
0.333333333 2
0.2%
0.344827586 1
0.1%
ValueCountFrequency (%)
9 1
0.1%
4.5 1
0.1%
3.75 1
0.1%
3.702702703 1
0.1%
3.125 1
0.1%
3 2
0.2%
2.909090909 1
0.1%
2.8 1
0.1%
2.75 1
0.1%
2.666666667 2
0.2%

Rank
Real number (ℝ)

HIGH CORRELATION 

Distinct228
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean590.50921
Minimum1
Maximum1194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-16T16:00:06.350203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile58
Q1290
median597.5
Q3892
95-th percentile1132
Maximum1194
Range1193
Interquartile range (IQR)602

Descriptive statistics

Standard deviation347.47551
Coefficient of variation (CV)0.58843369
Kurtosis-1.2034811
Mean590.50921
Median Absolute Deviation (MAD)294.5
Skewness-0.00055698309
Sum705068
Variance120739.23
MonotonicityNot monotonic
2023-07-16T16:00:06.429303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 47
 
3.9%
365 37
 
3.1%
434 33
 
2.8%
125 33
 
2.8%
160 30
 
2.5%
1012 27
 
2.3%
235 26
 
2.2%
750 26
 
2.2%
512 23
 
1.9%
479 22
 
1.8%
Other values (218) 890
74.5%
ValueCountFrequency (%)
1 1
 
0.1%
2 3
 
0.3%
5 2
 
0.2%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
10 11
0.9%
21 1
 
0.1%
22 21
1.8%
43 6
 
0.5%
ValueCountFrequency (%)
1194 1
 
0.1%
1192 2
 
0.2%
1191 1
 
0.1%
1190 1
 
0.1%
1189 1
 
0.1%
1186 3
0.3%
1185 1
 
0.1%
1181 4
0.3%
1176 5
0.4%
1169 7
0.6%

Image_URL
Text

UNIQUE 

Distinct1194
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2023-07-16T16:00:07.082119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length84
Median length81
Mean length65.181742
Min length59

Characters and Unicode

Total characters77827
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1194 ?
Unique (%)100.0%

Sample

1st rowhttps://img.pokemondb.net/sprites/sword-shield/icon/bulbasaur.png
2nd rowhttps://img.pokemondb.net/sprites/sword-shield/icon/ivysaur.png
3rd rowhttps://img.pokemondb.net/sprites/sword-shield/icon/venusaur.png
4th rowhttps://img.pokemondb.net/sprites/sword-shield/icon/venusaur-mega.png
5th rowhttps://img.pokemondb.net/sprites/sword-shield/icon/charmander.png
ValueCountFrequency (%)
https://img.pokemondb.net/sprites/sword-shield/icon/bulbasaur.png 1
 
0.1%
https://img.pokemondb.net/sprites/sword-shield/icon/venusaur-mega.png 1
 
0.1%
https://img.pokemondb.net/sprites/sword-shield/icon/charmeleon.png 1
 
0.1%
https://img.pokemondb.net/sprites/sword-shield/icon/charizard.png 1
 
0.1%
https://img.pokemondb.net/sprites/sword-shield/icon/charizard-mega-x.png 1
 
0.1%
https://img.pokemondb.net/sprites/sword-shield/icon/charizard-mega-y.png 1
 
0.1%
https://img.pokemondb.net/sprites/sword-shield/icon/squirtle.png 1
 
0.1%
https://img.pokemondb.net/sprites/sword-shield/icon/wartortle.png 1
 
0.1%
https://img.pokemondb.net/sprites/sword-shield/icon/blastoise.png 1
 
0.1%
https://img.pokemondb.net/sprites/sword-shield/icon/blastoise-mega.png 1
 
0.1%
Other values (1184) 1184
99.2%
2023-07-16T16:00:07.821777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 7164
 
9.2%
s 6346
 
8.2%
e 5846
 
7.5%
t 5614
 
7.2%
o 5609
 
7.2%
i 5540
 
7.1%
n 5413
 
7.0%
p 5048
 
6.5%
d 3619
 
4.7%
. 3582
 
4.6%
Other values (26) 24046
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 64405
82.8%
Other Punctuation 11940
 
15.3%
Dash Punctuation 1475
 
1.9%
Decimal Number 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 6346
9.9%
e 5846
 
9.1%
t 5614
 
8.7%
o 5609
 
8.7%
i 5540
 
8.6%
n 5413
 
8.4%
p 5048
 
7.8%
d 3619
 
5.6%
r 3190
 
5.0%
m 2788
 
4.3%
Other values (16) 15392
23.9%
Decimal Number
ValueCountFrequency (%)
0 2
28.6%
1 1
14.3%
5 1
14.3%
3 1
14.3%
4 1
14.3%
2 1
14.3%
Other Punctuation
ValueCountFrequency (%)
/ 7164
60.0%
. 3582
30.0%
: 1194
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
- 1475
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64405
82.8%
Common 13422
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 6346
9.9%
e 5846
 
9.1%
t 5614
 
8.7%
o 5609
 
8.7%
i 5540
 
8.6%
n 5413
 
8.4%
p 5048
 
7.8%
d 3619
 
5.6%
r 3190
 
5.0%
m 2788
 
4.3%
Other values (16) 15392
23.9%
Common
ValueCountFrequency (%)
/ 7164
53.4%
. 3582
26.7%
- 1475
 
11.0%
: 1194
 
8.9%
0 2
 
< 0.1%
1 1
 
< 0.1%
5 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 7164
 
9.2%
s 6346
 
8.2%
e 5846
 
7.5%
t 5614
 
7.2%
o 5609
 
7.2%
i 5540
 
7.1%
n 5413
 
7.0%
p 5048
 
6.5%
d 3619
 
4.7%
. 3582
 
4.6%
Other values (26) 24046
30.9%

Interactions

2023-07-16T16:00:02.470578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:56.428409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:57.053449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:57.835169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.490068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.113159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.777191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:00.399512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.173247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.837615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:02.529907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:56.487903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:57.112578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:57.897918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.549920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.178234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.836649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:00.463524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.236077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.895267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:02.590616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:56.545545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:57.173036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:57.959865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.609453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.242907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.897464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:00.530595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.299947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.969833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:02.661397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:56.610500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:57.237780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.028316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.676375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.312180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.963256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:00.598978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.368529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:02.036501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:02.733445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:56.670439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:57.457095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.090743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.737098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.375917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:00.023524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:00.664742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.433562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:02.096226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:02.806650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:56.734903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:57.521494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.159910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.802651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.445208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:00.087025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:00.735197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.501062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:02.159331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:02.867438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:56.795853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:57.580342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.221841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.859201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.506913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:00.143716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:00.798076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.564504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:02.220512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:02.936624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:56.863581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:57.648159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.293274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.926815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.577387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:00.208886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:00.870530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.636814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:02.285462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:03.007234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:56.931331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:57.715350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.360752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.992289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.647441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:00.274822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.044999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.706831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:02.350449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:03.065204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:56.989185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:57.772894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:58.424473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.051103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T15:59:59.710832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:00.333382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.108036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:01.768664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-16T16:00:02.405620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-07-16T16:00:07.918845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
TotalHPAttackDefenseSp. AtkSp. DefSpeedAverage StatsAtk-Def RatioRankType1Type2
Total1.0000.7290.7260.6830.7160.7380.5461.0000.085-1.0000.1480.115
HP0.7291.0000.5880.4770.4530.4880.2620.7290.136-0.7290.0740.097
Attack0.7260.5881.0000.5280.3230.3250.3690.7260.500-0.7260.1270.108
Defense0.6830.4770.5281.0000.3140.5930.0770.683-0.409-0.6830.1190.140
Sp. Atk0.7160.4530.3230.3141.0000.5760.4240.7160.042-0.7160.1330.082
Sp. Def0.7380.4880.3250.5930.5761.0000.2760.738-0.223-0.7380.0980.086
Speed0.5460.2620.3690.0770.4240.2761.0000.5460.321-0.5460.0930.126
Average Stats1.0000.7290.7260.6830.7160.7380.5461.0000.085-1.0000.1490.115
Atk-Def Ratio0.0850.1360.500-0.4090.042-0.2230.3210.0851.000-0.0850.1240.142
Rank-1.000-0.729-0.726-0.683-0.716-0.738-0.546-1.000-0.0851.0000.1450.115
Type10.1480.0740.1270.1190.1330.0980.0930.1490.1240.1451.0000.184
Type20.1150.0970.1080.1400.0820.0860.1260.1150.1420.1150.1841.000

Missing values

2023-07-16T16:00:03.164162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-16T16:00:03.297456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NamesType1Type2TotalHPAttackDefenseSp. AtkSp. DefSpeedAverage StatsType CombinationAtk-Def RatioRankImage_URL
0BulbasaurGrassPoison31845494965654553.000000Grass-Poison1.000000941https://img.pokemondb.net/sprites/sword-shield/icon/bulbasaur.png
1IvysaurGrassPoison40560626380806067.500000Grass-Poison0.984127750https://img.pokemondb.net/sprites/sword-shield/icon/ivysaur.png
2VenusaurGrassPoison5258082831001008087.500000Grass-Poison0.987952265https://img.pokemondb.net/sprites/sword-shield/icon/venusaur.png
3Mega VenusaurGrassPoison6258010012312212080104.166667Grass-Poison0.81300862https://img.pokemondb.net/sprites/sword-shield/icon/venusaur-mega.png
4CharmanderFireNaN30939524360506551.500000Fire1.209302977https://img.pokemondb.net/sprites/sword-shield/icon/charmander.png
5CharmeleonFireNaN40558645880658067.500000Fire1.103448750https://img.pokemondb.net/sprites/sword-shield/icon/charmeleon.png
6CharizardFireFlying5347884781098510089.000000Fire-Flying1.076923228https://img.pokemondb.net/sprites/sword-shield/icon/charizard.png
7Mega Charizard XFireDragon6347813011113085100105.666667Fire-Dragon1.17117156https://img.pokemondb.net/sprites/sword-shield/icon/charizard-mega-x.png
8Mega Charizard YFireFlying6347810478159115100105.666667Fire-Flying1.33333356https://img.pokemondb.net/sprites/sword-shield/icon/charizard-mega-y.png
9SquirtleWaterNaN31444486550644352.333333Water0.738462954https://img.pokemondb.net/sprites/sword-shield/icon/squirtle.png
NamesType1Type2TotalHPAttackDefenseSp. AtkSp. DefSpeedAverage StatsType CombinationAtk-Def RatioRankImage_URL
1184Wo- ChienDarkGrass5708585100951357095.000000Dark-Grass0.850000160https://img.pokemondb.net/sprites/scarlet-violet/icon/wo-chien.png
1185Chien- PaoDarkIce5708012080906513595.000000Dark-Ice1.500000160https://img.pokemondb.net/sprites/scarlet-violet/icon/chien-pao.png
1186Ting- LuDarkGround57015511012555804595.000000Dark-Ground0.880000160https://img.pokemondb.net/sprites/scarlet-violet/icon/ting-lu.png
1187Chi- YuDarkFire57055808013512010095.000000Dark-Fire1.000000160https://img.pokemondb.net/sprites/scarlet-violet/icon/chi-yu.png
1188Roaring MoonDragonDark590105139715510111998.333333Dragon-Dark1.957746118https://img.pokemondb.net/sprites/scarlet-violet/icon/roaring-moon.png
1189Iron ValiantFairyFighting59074130901206011698.333333Fairy-Fighting1.444444118https://img.pokemondb.net/sprites/scarlet-violet/icon/iron-valiant.png
1190KoraidonFightingDragon67010013511585100135111.666667Fighting-Dragon1.17391343https://img.pokemondb.net/sprites/scarlet-violet/icon/koraidon.png
1191MiraidonElectricDragon67010085100135115135111.666667Electric-Dragon0.85000043https://img.pokemondb.net/sprites/scarlet-violet/icon/miraidon.png
1192Walking WakeWaterDragon5909983911258310998.333333Water-Dragon0.912088118https://img.pokemondb.net/sprites/scarlet-violet/icon/walking-wake.png
1193Iron LeavesGrassPsychic59090130887010810498.333333Grass-Psychic1.477273118https://img.pokemondb.net/sprites/scarlet-violet/icon/iron-leaves.png